DBMS > Oracle NoSQL vs. Pinecone vs. SWC-DB vs. TerarkDB

System Properties Comparison Oracle NoSQL vs. Pinecone vs. SWC-DB vs. TerarkDB

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Editorial information provided by DB-Engines

Name

Description

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

A managed, cloud-native vector database

A high performance, scalable Wide Column DBMS

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Document store
Key-value store
Relational DBMS

Vector DBMS

Wide column store

Key-value store

Secondary database models

Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	3.23
Rank	#92	Overall
	#3	Vector DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

www.oracle.com/database/nosql/technologies/nosql

www.pinecone.io

github.com/kashirin-alex/swc-db
www.swcdb.org

github.com/bytedance/terarkdb

Technical documentation

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.pinecone.io/docs/overview

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Oracle

Pinecone Systems, Inc

Alex Kashirin

ByteDance, originally Terark

Initial release

2011

2019

2020

2016

Current release

24.1, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

Open Source

GPL V3

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

Linux
Solaris SPARC/x86

hosted

Linux

Data scheme

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

optional

String, Number, Boolean

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

RESTful HTTP API

Proprietary protocol
Thrift

C++ API
Java API

Supported programming languages

C
C#
Go
Java
JavaScript (Node.js)
Python

Python

C++

C++
Java

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

off heap cache

yes

User concepts

Access control

Access rights for users and roles

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More resources

DB-Engines blog posts

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2 June 2023, Matthias Gelbmann

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